Abstract More than 80% of cancer patients who undergo immune checkpoint inhibitor (ICI) therapy experience immune related adverse events, including autoimmunity, following treatment. The etiology of autoimmune disease in humans is poorly understood and effective treatments are limited. Inbred mice are a valuable tool for understanding basic biological mechanisms of disease, but are less effective for understanding human autoimmunity. Therefore, it is critical to develop minimally invasive human models to provide real world insights into the mechanisms, development and therapy for autoimmunity. The widespread use of electronic health records (EHRs) in healthcare and the depth of data collected for cancer patients, presents an important opportunity to identify risk factors for the development of autoimmune disease following immunotherapy. Our project brings together a team of immunologists, oncologists, informaticists and machine learning experts working within an EHR network, to identify a cohort of cancer patients who have undergone ICI therapy. From this cohort we will design and implement a broad and deep longitudinal database of EHR data, including treatment and response data and laboratory results, to enable the development of phenotypic profiles and models for autoimmune disease development in humans. The overarching goal of this project proposal is to test the feasibility and effectiveness of using a hybrid in silico / in vivo model system combined with machine learning strategies as a platform for understanding the etiology of autoimmune disease. In the R61 Phase we propose to identify patients who develop rheumatoid arthritis (RA) in the in the presence or absence of cancer, and control cohort, using a physician-validated cohort of cancer patients and data from EHR, and use machine learning strategies to develop phenotypic profiles for RA in the presence or absence of cancer and ICI therapy. In the R33 Phase we will and develop and assess phenotypic profiles for global biomarkers tolerance disruption using machine learning and determine if family history is a predictor of the development of autoimmunity following ICI therapy. Our proposal, to develop an in silico based model for exploring the onset of autoimmunity, makes a leap forward for translational immunology and the exploration of mechanisms of human autoimmune disease development by leveraging the power of the information collected in EHR to predict outcomes. The phenotypic profiles developed could significantly accelerate precision medicine approaches for employing ICIs that minimize the potential autoimmune disease based on personal genetic, environmental and social information.